Malignant Transformation of Giant Cell Tumor of Bone and the Association with Denosumab Treatment:A Radiology and Pathology Perspective

Objective. Malignancy in giant cell tumor of bone (mGCTB) is categorized as primary (concomitantly with conventional GCTB) or secondary (after radiotherapy or other treatment). Denosumab therapy has been suggested to play a role in the etiology of secondary mGCTB. In this case series from a tertiary referral sarcoma center, we aimed to find distinctive features for malignant transformation in GCTB on different imaging modalities. Furthermore, we assessed the duration of denosumab treatment and lag time to the development of malignancy. Methods. From a histopathology database search, 6 patients were pathologically confirmed as having initial conventional GCTB and subsequently with secondary mGCTB. Results. At the time of mGCTB diagnosis, 2 cases were treated with denosumab only, 2 with denosumab and surgery, 1 with multiple curettages and radiotherapy, and 1 with surgery only. In the 4 denosumab treated patients, the mean lag time to malignant transformation was 7 months (range 2-11 months). Imaging findings suspicious of malignant transformation related to denosumab therapy are the absence of fibro-osseous matrix formation and absent neocortex formation on CT, and stable or even increased size of the soft tissue component. Conclusion. In 4 patients treated with denosumab, secondary mGCTB occurred within the first year after initiation of treatment. Radiotherapy-associated mGCTB has a longer lag time than denosumab-associated mGCTB. Close clinical and imaging follow-up during the first months of denosumab therapy is key, as mGCTB tends to have rapid aggressive behavior, similar to other high-grade sarcomas. Nonresponders should be (re) evaluated for their primary diagnosis of conventional GCTB

MRI of diffuse-type tenosynovial giant cell tumour in the knee: a guide for diagnosis and treatment response assessment

Key Points TGCT is categorised according to its site of involvement and growth pattern. D-TGCT is characterised by irregular synovial proliferation infiltrating multiple synovial knee recesses. Systematic MRI approach is provided for preoperative and systemic treatment response assessment. An overview of synovial knee recesses guides the radiologist in assessing blind spots. Machine learning-based tumour segmentation may be applied to assess D-TGCT tumour volume in the near future, so that treatment response assessment can be quantified and improved by artificial intelligence

Relationship Between Soil Properties and Banana Productivity in the Two Main Cultivation Areas in Venezuela

To identify the main edaphic variables most correlated to banana productivity in Venezuela and explore the development of an empirical correlation model to predict this productivity based on soil characteristics. Six agricultural fields located in two of the main banana production areas of Venezuela were selected. The experimental sites were in large farms (≥ 50 ha) with four productivity levels in “Gran Nain” bananas, with an area of 4 ha for each of four productive levels: High - High, High - Low, Low - High, and Low - Low. Sixty sampling points were used to characterize the soils under study. Additionally, a Productivity Index (PI) based on three different biometric data on plant productivity was proposed. Through hierarchical statistical analysis, the first 16 soil variables that best explained the PI were selected. Thus, five multiple linear regression models were estimated, using the stepwise regression method. Subsequently, a performance analysis was used to compare the prediction quality range and the error associated with the number of soil variables selected for the proposed models. The selected model included the following soil variables: Mg, penetration resistance, total microbial respiration, bulk density, and omnivorous free-living nematodes. These variables explain the PI with an R2 of 0.55, the mean absolute error (MAE) of 0.8, and the root of the mean squared error (RMSE) of 1.0. The five selected variables are proposed to characterize the soil Productivity Index in banana and could be used in a site-specific soil management program for the banana areas of Venezuela.Fil: Olivares, Barlin Orlando. Universidad de Córdoba; EspañaFil: Araya Alman, Miguel. Universidad Católica del Maule; ChileFil: Acevedo Opazo, Cesar. Universidad de Talca; ChileFil: Rey, Juan Carlos. Instituto Nacional de Investigaciones Agrícolas; VenezuelaFil: Cañete Salinas, Paulo. Universidad de Talca; ChileFil: Giannini Kurina, Franca. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigaciones Agropecuarias. Unidad de Fitopatología y Modelización Agrícola. Grupo Vinculado Catedra de Estadística y Biometría de la Facultad de Ciencias Agropecuarias de la Universidad Nacional de Córdoba al Ufyma | Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Unidad de Fitopatología y Modelización Agrícola. Grupo Vinculado Catedra de Estadística y Biometría de la Facultad de Ciencias Agropecuarias de la Universidad Nacional de Córdoba al Ufyma; ArgentinaFil: Balzarini, Monica Graciela. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigaciones Agropecuarias. Unidad de Fitopatología y Modelización Agrícola - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Unidad de Fitopatología y Modelización Agrícola; ArgentinaFil: Lobo, Deyanira. Universidad Central de Venezuela; VenezuelaFil: Navas Cortés, Juan A.. Consejo Superior de Investigaciones Científicas; EspañaFil: Landa, Blanca B.. Consejo Superior de Investigaciones Científicas; EspañaFil: Gómez, José Alfonso. Consejo Superior de Investigaciones Científicas; Españ